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  <title>Description of filterDoog</title>
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<h1>filterDoog
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<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>n-dim difference of offset Gaussian DooG filter (Gaussian derivative).</strong></div>

<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function dG = filterDoog( dims, sigmas, nderivs, show ) </strong></div>

<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> n-dim difference of offset Gaussian DooG filter (Gaussian derivative).

 Creates a nd derivative of Gaussian kernel.  For all but d==1 use
 primarily for visualization purposes -- for filtering better to use the
 indvidiual seperable kernels for efficiency purposes.

 USAGE
  dG = filterDoog( dims, sigmas, nderivs, [show] )

 INPUTS
  dims    - nd element vector of dimensions of final Gaussian
  sigmas  - sigmas for nd Gaussian
  nderivs - order of derivative along each of the nd dimensions
  show    - [0] figure to use for optional display

 OUTPUTS
  dG      - The derivative of Gaussian filter

 EXAMPLE
  dG1 = filterDoog( 43, 2, 3, 1 ); %1D
  dG2 = filterDoog( [41 41], [3 3], [1,1], 2 ); %2D
  dG3 = filterDoog( [101 101 101], [4,4,10], [1,1,0], 3 ); %3D

 See also <a href="filterGauss.html" class="code" title="function G = filterGauss( dims, mu, C, show )">FILTERGAUSS</a>, NORMPDF2, <a href="filterDog2d.html" class="code" title="function G = filterDog2d( r, var, order, show )">FILTERDOG2D</a>, <a href="filterGabor2d.html" class="code" title="function [Feven,Fodd] = filterGabor2d( r, sig, lam, theta, omega, show )">FILTERGABOR2D</a>

 Piotr's Computer Vision Matlab Toolbox      Version 2.0
 Copyright 2014 Piotr Dollar.  [pdollar-at-gmail.com]
 Licensed under the Simplified BSD License [see external/bsd.txt]</pre></div>





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